+
+ +
+

dpnp.linalg.svdvals

+
+
+dpnp.linalg.svdvals(x, /)[source]
+

Returns the singular values of a matrix (or a stack of matrices) x.

+

When x is a stack of matrices, the function will compute +the singular values for each matrix in the stack.

+

Calling dpnp.linalg.svdvals(x) to get singular values is the same as +dpnp.linalg.svd(x, compute_uv=False, hermitian=False).

+

For full documentation refer to numpy.linalg.svdvals.

+
+
Parameters:
+

x ((..., M, N) {dpnp.ndarray, usm_ndarray}) -- Input array with x.ndim >= 2 and whose last two dimensions +form matrices on which to perform singular value decomposition.

+
+
Returns:
+

out -- Vector(s) of singular values of length K, where K = min(M, N).

+
+
Return type:
+

(..., K) dpnp.ndarray

+
+
+
+

See also

+
+
dpnp.linalg.svd

Compute the singular value decomposition.

+
+
+
+

Examples

+
>>> import dpnp as np
+>>> a = np.array([[3, 0], [0, 4]])
+>>> np.linalg.svdvals(a)
+array([4., 3.])
+
+
+

This is equivalent to calling:

+
>>> np.linalg.svd(a, compute_uv=False, hermitian=False)
+array([4., 3.])
+
+
+

Stack of matrices:

+
>>> b = np.array([[[6, 0], [0, 8]], [[9, 0], [0, 12]]])
+>>> np.linalg.svdvals(b)
+array([[ 8.,  6.],
+       [12.,  9.]])
+
+
+
+ +
+ + +
+